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Data X:
14 25 19 33 17 29 17 NA 15 NA 20 35 15 NA 19 NA 15 NA 15 25 19 33 NA 12 20 35 18 31 15 26 14 25 20 NA NA NA 16 NA 16 NA 16 28 10 NA 19 33 19 NA 16 NA 15 NA 18 32 17 30 19 33 17 29 NA 6 19 33 20 35 5 NA 19 33 16 28 15 NA 16 28 18 31 16 28 15 NA 17 29 NA 7 20 35 19 33 7 12 13 NA 16 28 16 28 NA NA 18 31 18 NA 16 NA 17 29 19 33 16 28 19 NA 13 22 16 28 13 NA 12 20 17 30 17 29 17 30 16 28 16 28 14 24 16 28 13 24 16 28 14 NA 20 NA 12 NA 13 NA 18 NA 14 24 19 33 18 32 14 24 18 31 19 NA 15 25 14 NA 17 29 19 NA 13 24 19 33 18 31 20 NA 15 25 15 26 15 NA 20 35 15 NA 19 33 18 NA 18 31 15 26 20 35 17 29 12 NA 18 NA 19 33 20 35 NA NA 17 NA 15 NA 16 NA 18 NA 18 31 14 NA 15 NA 12 NA 17 30 14 NA 18 31 17 29 17 NA 20 35 16 27 14 24 15 26 18 NA 20 35 17 NA 17 29 17 30 17 NA 15 NA 17 NA 18 31 17 NA 20 35 15 NA 16 28 15 NA 18 32 11 NA 15 26 18 31 20 35 19 NA 14 24 16 NA 15 25 17 29 18 31 20 35 17 29 18 NA 15 NA 16 27 11 19 15 27 18 32 17 NA 16 29 12 NA 19 NA 18 31 15 25 17 29 19 33 18 NA 19 NA 16 NA 16 28 16 NA 14 26
Names of X columns:
ITHSUM TVDCSUM
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grey
grey
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Omit all rows with missing values?
no
no
yes
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R Code
if(par2=='yes') { z <- na.omit(as.data.frame(t(y))) } else { z <- as.data.frame(t(y)) } bitmap(file='test1.png') (r<-boxplot(z ,xlab=xlab,ylab=ylab,main=main,notch=TRUE,col=par1)) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Boxplot statistics',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',1,TRUE) a<-table.element(a,'lower whisker',1,TRUE) a<-table.element(a,'lower hinge',1,TRUE) a<-table.element(a,'median',1,TRUE) a<-table.element(a,'upper hinge',1,TRUE) a<-table.element(a,'upper whisker',1,TRUE) a<-table.row.end(a) for (i in 1:length(y[,1])) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],1,TRUE) for (j in 1:5) { a<-table.element(a,r$stats[j,i]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Boxplot Notches',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',1,TRUE) a<-table.element(a,'lower bound',1,TRUE) a<-table.element(a,'median',1,TRUE) a<-table.element(a,'upper bound',1,TRUE) a<-table.row.end(a) for (i in 1:length(y[,1])) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],1,TRUE) a<-table.element(a,r$conf[1,i]) a<-table.element(a,r$stats[3,i]) a<-table.element(a,r$conf[2,i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
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Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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